Name
Title | ||
|---|---|---|
A Network-Based Approach to Modeling and Predicting Product Coconsideration Relations. |
Abstract | ||
|---|---|---|
Understanding customer preferences in consideration decisions is critical to choice modeling in engineering design. While existing literature has shown that the exogenous effects (e.g., product and customer attributes) are deciding factors in customers' consideration decisions, it is not clear how the endogenous effects (e.g., the intercompetition among products) would influence such decisions. This paper presents a network-based approach based on Exponential Random Graph Models to study customers' consideration behaviors according to engineering design. Our proposed approach is capable of modeling the endogenous effects among products through various network structures (e.g., stars and triangles) besides the exogenous effects and predicting whether two products would be conisdered together. To assess the proposed model, we compare it against the dyadic network model that only considers exogenous effects. Using buyer survey data from the China automarket in 2013 and 2014, we evaluate the goodness of fit and the predictive power of the two models. The results show that our model has a better fit and predictive accuracy than the dyadic network model. This underscores the importance of the endogenous effects on customers' consideration decisions. The insights gained from this research help explain how endogenous effects interact with exogeous effects in affecting customers' decision-making. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1155/2018/2753638 | COMPLEXITY |
Field | DocType | Volume |
Survey data collection,Predictive power,Engineering design process,Artificial intelligence,Exponential random graph models,Goodness of fit,Network model,Mathematics,Machine learning,Network structure | Journal | 2018 |
ISSN | Citations | PageRank |
1076-2787 | 1 | 0.35 |
References | Authors | |
9 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenghui Sha | 1 | 2 | 1.40 |
| Yun Huang | 2 | 118 | 11.29 |
| Jiawei Sophia Fu | 3 | 1 | 0.35 |
| Mingxian Wang | 4 | 1 | 0.35 |
| Yan Fu | 5 | 1 | 0.69 |
| Noshir S. Contractor | 6 | 507 | 61.05 |
| Wei Chen | 7 | 49 | 19.09 |